Customized hash algorithms

ABSTRACT

A storage system determines source addresses, and destination addresses in a storage system, for network traffic. The storage system determines a hash algorithm, from a plurality of hash algorithms. The hash algorithm is to be used across the source addresses for load-balancing the network traffic to the destination addresses. The storage system determines that the hash algorithm more closely meets one or more load-balancing criteria than at least one other hash algorithm, of the plurality of hash algorithms. The storage system distributes the network traffic from the source addresses to the destination addresses in the storage system, with load-balancing according to the determined hash algorithm.

TECHNICAL FIELD

The technical field to which the invention relates is data storagesystems, and more specifically to load-balancing in storage systems.

BACKGROUND

As storage platforms scale, the amount of storage memory and the numberof network connections among storage components, computing componentsand switches may increase. As a result, newer storage systemarchitectures emerge. However, communication bottlenecks and other datathroughput limiting circumstances may arise in connecting newer storagesystem architectures to customer legacy networks. It is in thisenvironment that storage system embodiments described herein presenttechnological solutions to these technological problems.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 4 sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 5 illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 6A is a block diagram of a further embodiment of the storagecluster having one example of connectivity between and within storagenodes and storage units in accordance with some embodiments.

FIG. 6B is a variation of the connectivity within the storage cluster ofFIG. 6A in accordance with some embodiments.

FIG. 7 is a block diagram of a further embodiment of the storage clusterof FIGS. 1-5 , suitable for data storage or a combination of datastorage and computing in accordance with some embodiments.

FIG. 8A is a block diagram of a further embodiment of connectivitywithin the storage cluster of FIGS. 1-5 , with switches in accordancewith some embodiments.

FIG. 8B is a variation of connectivity within the storage cluster ofFIG. 8A, with the switches coupling the storage units in accordance withsome embodiments.

FIG. 9A is a block diagram of one example of an architecture for computenodes coupled together for the storage cluster in accordance with someembodiments.

FIG. 9B is a block diagram of a further embodiment of the storagecluster of FIGS. 1-5 , with the compute nodes of FIG. 9A in accordancewith some embodiments.

FIG. 9C is a block diagram of a variation of the storage cluster withcompute nodes of FIG. 9B, depicting storage nodes, storage units andcompute nodes in multiple chassis, all coupled together as one or morestorage clusters and variations of connectivity within a chassis andbetween chassis in accordance with some embodiments.

FIG. 9D is a block diagram of an embodiment of a multi-chassis storagecluster, depicting multilevel load-balancing in accordance with someembodiments.

FIG. 10A is a flow diagram of a method for operating a storage cluster,which can be practiced on or by embodiments of the storage cluster,storage nodes and/or non-volatile solid state storages or storage unitsin accordance with some embodiments.

FIG. 10B is a flow diagram of a method of load-balancing for a storagesystem in accordance with some embodiments.

FIG. 11 is an illustration showing an exemplary computing device whichmay implement the embodiments described herein.

FIG. 12 illustrates a deployment model for a storage system inaccordance with some embodiments of the present disclosure.

FIG. 13 illustrates an active/passive link aggregation group (LAG) in astorage system embodiment.

FIG. 14 illustrates a multi-chassis link aggregation group (MLAG) in astorage system embodiment.

FIG. 15 illustrates a balancing process using virtual media accesscontrol (MAC) addresses, as applicable to embodiments of storagesystems.

FIG. 16A illustrates a flow diagram of a method for a storage system,which is suitable for the storage system embodiment in FIG. 14 .

FIG. 16B illustrates a flow diagram of a method for a storage system,which is suitable for the storage system embodiments herein and furtherstorage systems, and uses the balancing process depicted herein.

FIG. 17 illustrates a storage system that selects from among multiplehash algorithms and performs load-balancing according to the selectedhash algorithm, in accordance with an embodiment of the presentdisclosure.

FIG. 18 illustrates usage of the selected hash algorithm inload-balancing, in accordance with an embodiment of the presentdisclosure.

FIG. 19 is a flow diagram of a method performed by a storage system,which uses the hash algorithm selection depicted in FIGS. 17 and 18 orvariation thereof in accordance with an embodiment of the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for storage systems andscale-out storage platforms in accordance with embodiments of thepresent disclosure are described with reference to the accompanyingdrawings, beginning with FIG. 1A. FIG. 1A illustrates an example systemfor data storage, in accordance with some implementations. System 100(also referred to as “storage system” herein) includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat system 100 may include the same, more, or fewer elements configuredin the same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119A-D may be aCPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA, flash memory 206, and NVRAM 204 (which issuper-capacitor backed DRAM 216, see FIGS. 2B and 2C) on a PCIe(peripheral component interconnect express) board in a chassis 138 (seeFIG. 2A). The storage unit 152 may be implemented as a single boardcontaining storage, and may be the largest tolerable failure domaininside the chassis. In some embodiments, up to two storage units 152 mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS'environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage array 306 and remote, cloud-based storage thatis utilized by the storage array 306. Through the use of a cloud storagegateway, organizations may move primary iSCSI or NAS to the cloudservices provider 302, thereby enabling the organization to save spaceon their on-premises storage systems. Such a cloud storage gateway maybe configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache where data is initially written to storage resourceswith relatively fast write latencies, relatively high write bandwidth,or similar characteristics. In such an example, data that is written tothe storage resources that serve as a write cache may later be writtento other storage resources that may be characterized by slower writelatencies, lower write bandwidth, or similar characteristics than thestorage resources that are utilized to serve as a write cache. In asimilar manner, storage resources within the storage system may beutilized as a read cache, where the read cache is populated inaccordance with a set of predetermined rules or heuristics. In otherembodiments, tiering may be achieved within the storage systems byplacing data within the storage system in accordance with one or morepolicies such that, for example, data that is accessed frequently isstored in faster storage tiers while data that is accessed infrequentlyis stored in slower storage tiers.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques to preserve the integrity of data that is storedwithin the storage systems. Readers will appreciate that such dataprotection techniques may be carried out, for example, by systemsoftware executing on computer hardware within the storage system, by acloud services provider, or in other ways. Such data protectiontechniques can include, for example, data archiving techniques thatcause data that is no longer actively used to be moved to a separatestorage device or separate storage system for long-term retention, databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe with the storagesystem, data replication techniques through which data stored in thestorage system is replicated to another storage system such that thedata may be accessible via multiple storage systems, data snapshottingtechniques through which the state of data within the storage system iscaptured at various points in time, data and database cloning techniquesthrough which duplicate copies of data and databases may be created, andother data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

For further explanation, FIG. 4 sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 4 , the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’), Microsoft Azure, GoogleCloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.For example, the cloud-based storage system 318 may be used to provideblock storage services to users of the cloud-based storage system 318,the cloud-based storage system 318 may be used to provide storageservices to users of the cloud-based storage system 318 through the useof solid-state storage, and so on.

The cloud-based storage system 318 depicted in FIG. 4 includes two cloudcomputing instances 320, 322 that each are used to support the executionof a storage controller application 324, 326. The cloud computinginstances 320, 322 may be embodied, for example, as instances of cloudcomputing resources (e.g., virtual machines) that may be provided by thecloud computing environment 316 to support the execution of softwareapplications such as the storage controller application 324, 326. In oneembodiment, the cloud computing instances 320, 322 may be embodied asAmazon Elastic Compute Cloud (‘EC2’) instances. In such an example, anAmazon Machine Image (‘AMI’) that includes the storage controllerapplication 324, 326 may be booted to create and configure a virtualmachine that may execute the storage controller application 324, 326.

In the example method depicted in FIG. 4 , the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data received from the users of the cloud-basedstorage system 318 to the cloud-based storage system 318, erasing datafrom the cloud-based storage system 318, retrieving data from thecloud-based storage system 318 and providing such data to users of thecloud-based storage system 318, monitoring and reporting of diskutilization and performance, performing redundancy operations, such asRAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 4 may include identical source code that is executedwithin different cloud computing instances 320, 322.

Consider an example in which the cloud computing environment 316 isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, the cloud computing instance 320 thatoperates as the primary controller may be deployed on one of theinstance types that has a relatively large amount of memory andprocessing power while the cloud computing instance 322 that operates asthe secondary controller may be deployed on one of the instance typesthat has a relatively small amount of memory and processing power. Insuch an example, upon the occurrence of a failover event where the rolesof primary and secondary are switched, a double failover may actually becarried out such that: 1) a first failover event where the cloudcomputing instance 322 that formerly operated as the secondarycontroller begins to operate as the primary controller, and 2) a thirdcloud computing instance (not shown) that is of an instance type thathas a relatively large amount of memory and processing power is spun upwith a copy of the storage controller application, where the third cloudcomputing instance begins operating as the primary controller while thecloud computing instance 322 that originally operated as the secondarycontroller begins operating as the secondary controller again. In suchan example, the cloud computing instance 320 that formerly operated asthe primary controller may be terminated. Readers will appreciate thatin alternative embodiments, the cloud computing instance 320 that isoperating as the secondary controller after the failover event maycontinue to operate as the secondary controller and the cloud computinginstance 322 that operated as the primary controller after theoccurrence of the failover event may be terminated once the primary rolehas been assumed by the third cloud computing instance (not shown).

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance 320 operates asthe primary controller and the second cloud computing instance 322operates as the secondary controller, other embodiments are within thescope of the present disclosure. For example, each cloud computinginstance 320, 322 may operate as a primary controller for some portionof the address space supported by the cloud-based storage system 318,each cloud computing instance 320, 322 may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system 318 are divided in some other way, and so on.In fact, in other embodiments where costs savings may be prioritizedover performance demands, only a single cloud computing instance mayexist that contains the storage controller application.

The cloud-based storage system 318 depicted in FIG. 4 includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n depicted in FIG.4 may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment 316 tosupport the execution of software applications. The cloud computinginstances 340 a, 340 b, 340 n of FIG. 4 may differ from the cloudcomputing instances 320, 322 described above as the cloud computinginstances 340 a, 340 b, 340 n of FIG. 4 have local storage 330, 334, 338resources whereas the cloud computing instances 320, 322 that supportthe execution of the storage controller application 324, 326 need nothave local storage resources. The cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 may be embodied, for example,as EC2 M5 instances that include one or more SSDs, as EC2 R5 instancesthat include one or more SSDs, as EC2 I3 instances that include one ormore SSDs, and so on. In some embodiments, the local storage 330, 334,338 must be embodied as solid-state storage (e.g., SSDs) rather thanstorage that makes use of hard disk drives.

In the example depicted in FIG. 4 , each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 4 , each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block-storage 342, 344, 346 that is offered by the cloudcomputing environment 316. The block-storage 342, 344, 346 that isoffered by the cloud computing environment 316 may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume may be coupled to a first cloud computing instance 340a, a second EBS volume may be coupled to a second cloud computinginstance 340 b, and a third EBS volume may be coupled to a third cloudcomputing instance 340 n. In such an example, the block-storage 342,344, 346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block-storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 4 , the cloud computing instances 340 a,340 b, 340 n with local storage 330, 334, 338 may be utilized, by cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 to service I/O operations that aredirected to the cloud-based storage system 318. Consider an example inwhich a first cloud computing instance 320 that is executing the storagecontroller application 324 is operating as the primary controller. Insuch an example, the first cloud computing instance 320 that isexecuting the storage controller application 324 may receive (directlyor indirectly via the secondary controller) requests to write data tothe cloud-based storage system 318 from users of the cloud-based storagesystem 318. In such an example, the first cloud computing instance 320that is executing the storage controller application 324 may performvarious tasks such as, for example, deduplicating the data contained inthe request, compressing the data contained in the request, determiningwhere to the write the data contained in the request, and so on, beforeultimately sending a request to write a deduplicated, encrypted, orotherwise possibly updated version of the data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. Either cloud computing instance 320, 322, in some embodiments,may receive a request to read data from the cloud-based storage system318 and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

Readers will appreciate that when a request to write data is received bya particular cloud computing instance 340 a, 340 b, 340 n with localstorage 330, 334, 338, the software daemon 328, 332, 336 or some othermodule of computer program instructions that is executing on theparticular cloud computing instance 340 a, 340 b, 340 n may beconfigured to not only write the data to its own local storage 330, 334,338 resources and any appropriate block-storage 342, 344, 346 that areoffered by the cloud computing environment 316, but the software daemon328, 332, 336 or some other module of computer program instructions thatis executing on the particular cloud computing instance 340 a, 340 b,340 n may also be configured to write the data to cloud-based objectstorage 348 that is attached to the particular cloud computing instance340 a, 340 b, 340 n. The cloud-based object storage 348 that is attachedto the particular cloud computing instance 340 a, 340 b, 340 n may beembodied, for example, as Amazon Simple Storage Service (‘S3’) storagethat is accessible by the particular cloud computing instance 340 a, 340b, 340 n. In other embodiments, the cloud computing instances 320, 322that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348.

Readers will appreciate that, as described above, the cloud-basedstorage system 318 may be used to provide block storage services tousers of the cloud-based storage system 318. While the local storage330, 334, 338 resources and the block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 nmay support block-level access, the cloud-based object storage 348 thatis attached to the particular cloud computing instance 340 a, 340 b, 340n supports only object-based access. In order to address this, thesoftware daemon 328, 332, 336 or some other module of computer programinstructions that is executing on the particular cloud computinginstance 340 a, 340 b, 340 n may be configured to take blocks of data,package those blocks into objects, and write the objects to thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 or some other moduleof computer program instructions that is executing on the particularcloud computing instance 340 a, 340 b, 340 n may be configured to: 1)create a first object that includes the first 1 MB of data and write thefirst object to the cloud-based object storage 348, 2) create a secondobject that includes the second 1 MB of data and write the second objectto the cloud-based object storage 348, 3) create a third object thatincludes the third 1 MB of data and write the third object to thecloud-based object storage 348, and so on. As such, in some embodiments,each object that is written to the cloud-based object storage 348 may beidentical (or nearly identical) in size. Readers will appreciate that insuch an example, metadata that is associated with the data itself may beincluded in each object (e.g., the first 1 MB of the object is data andthe remaining portion is metadata associated with the data).

Readers will appreciate that the cloud-based object storage 348 may beincorporated into the cloud-based storage system 318 to increase thedurability of the cloud-based storage system 318. Continuing with theexample described above where the cloud computing instances 340 a, 340b, 340 n are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 as the only source ofpersistent data storage in the cloud-based storage system 318 may resultin a relatively unreliable storage system. Likewise, EBS volumes aredesigned for 99.999% availability. As such, even relying on EBS as thepersistent data store in the cloud-based storage system 318 may resultin a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system 318 that can incorporate S3 into its pool ofstorage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system 318 thatcan incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system 318 depicted in FIG. 4 not only stores data in S3 but thecloud-based storage system 318 also stores data in local storage 330,334, 338 resources and block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, such thatread operations can be serviced from local storage 330, 334, 338resources and the block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, therebyreducing read latency when users of the cloud-based storage system 318attempt to read data from the cloud-based storage system 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block-storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

As described above, when the cloud computing instances 340 a, 340 b, 340n with local storage 330, 334, 338 are embodied as EC2 instances, thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 are only guaranteed to have a monthly uptime of 99.9% and datastored in the local instance store only persists during the lifetime ofeach cloud computing instance 340 a, 340 b, 340 n with local storage330, 334, 338. As such, one or more modules of computer programinstructions that are executing within the cloud-based storage system318 (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 by creating one or more new cloudcomputing instances with local storage, retrieving data that was storedon the failed cloud computing instances 340 a, 340 b, 340 n from thecloud-based object storage 348, and storing the data retrieved from thecloud-based object storage 348 in local storage on the newly createdcloud computing instances. Readers will appreciate that many variants ofthis process may be implemented.

Consider an example in which all cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 failed. In such an example, themonitoring module may create new cloud computing instances with localstorage, where high-bandwidth instances types are selected that allowfor the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage 348. Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage 348 such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage 348 asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage348, less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system 318. The number ofnew cloud computing instances that are created may substantially exceedthe number of cloud computing instances that are needed to locally storeall of the data stored by the cloud-based storage system 318 in order tomore rapidly pull data from the cloud-based object storage 348 and intothe new cloud computing instances, as each new cloud computing instancecan (in parallel) retrieve some portion of the data stored by thecloud-based storage system 318. In such embodiments, once the datastored by the cloud-based storage system 318 has been pulled into thenewly created cloud computing instances, the data may be consolidatedwithin a subset of the newly created cloud computing instances and thosenewly created cloud computing instances that are excessive may beterminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system 318 have written to the cloud-based storage system 318.In such an example, assume that all 1,000 cloud computing instancesfail. In such an example, the monitoring module may cause 100,000 cloudcomputing instances to be created, where each cloud computing instanceis responsible for retrieving, from the cloud-based object storage 348,distinct 1/100,000th chunks of the valid data that users of thecloud-based storage system 318 have written to the cloud-based storagesystem 318 and locally storing the distinct chunk of the dataset that itretrieved. In such an example, because each of the 100,000 cloudcomputing instances can retrieve data from the cloud-based objectstorage 348 in parallel, the caching layer may be restored 100 timesfaster as compared to an embodiment where the monitoring module onlycreate 1000 replacement cloud computing instances. In such an example,over time the data that is stored locally in the 100,000 could beconsolidated into 1,000 cloud computing instances and the remaining99,000 cloud computing instances could be terminated.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Consider anexample in which the monitoring module monitors the performance of thecould-based storage system 318 via communications with one or more ofthe cloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326, via monitoringcommunications between cloud computing instances 320, 322, 340 a, 340 b,340 n, via monitoring communications between cloud computing instances320, 322, 340 a, 340 b, 340 n and the cloud-based object storage 348, orin some other way. In such an example, assume that the monitoring moduledetermines that the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318. In such anexample, the monitoring module may create a new, more powerful cloudcomputing instance (e.g., a cloud computing instance of a type thatincludes more processing power, more memory, etc. . . . ) that includesthe storage controller application such that the new, more powerfulcloud computing instance can begin operating as the primary controller.Likewise, if the monitoring module determines that the cloud computinginstances 320, 322 that are used to support the execution of a storagecontroller application 324, 326 are oversized and that cost savingscould be gained by switching to a smaller, less powerful cloud computinginstance, the monitoring module may create a new, less powerful (andless expensive) cloud computing instance that includes the storagecontroller application such that the new, less powerful cloud computinginstance can begin operating as the primary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system 318, an example in which the monitoring module determinesthat the utilization of the local storage that is collectively providedby the cloud computing instances 340 a, 340 b, 340 n has reached apredetermined utilization threshold (e.g., 95%). In such an example, themonitoring module may create additional cloud computing instances withlocal storage to expand the pool of local storage that is offered by thecloud computing instances. Alternatively, the monitoring module maycreate one or more new cloud computing instances that have largeramounts of local storage than the already existing cloud computinginstances 340 a, 340 b, 340 n, such that data stored in an alreadyexisting cloud computing instance 340 a, 340 b, 340 n can be migrated tothe one or more new cloud computing instances and the already existingcloud computing instance 340 a, 340 b, 340 n can be terminated, therebyexpanding the pool of local storage that is offered by the cloudcomputing instances. Likewise, if the pool of local storage that isoffered by the cloud computing instances is unnecessarily large, datacan be consolidated and some cloud computing instances can beterminated.

Readers will appreciate that the cloud-based storage system 318 may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system 318, but themonitoring module may also apply predictive policies that are based on,for example, observed behavior (e.g., every night from 10 PM until 6 AMusage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system 318 may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem 318 may be dynamically scaled, the cloud-based storage system 318may even operate in a way that is more dynamic. Consider the example ofgarbage collection. In a traditional storage system, the amount ofstorage is fixed. As such, at some point the storage system may beforced to perform garbage collection as the amount of available storagehas become so constrained that the storage system is on the verge ofrunning out of storage. In contrast, the cloud-based storage system 318described here can always ‘add’ additional storage (e.g., by adding morecloud computing instances with local storage). Because the cloud-basedstorage system 318 described here can always ‘add’ additional storage,the cloud-based storage system 318 can make more intelligent decisionsregarding when to perform garbage collection. For example, thecloud-based storage system 318 may implement a policy that garbagecollection only be performed when the number of IOPS being serviced bythe cloud-based storage system 318 falls below a certain level. In someembodiments, other system-level functions (e.g., deduplication,compression) may also be turned off and on in response to system load,given that the size of the cloud-based storage system 318 is notconstrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 3C.

In some embodiments, especially in embodiments where the cloud-basedobject storage 348 resources are embodied as Amazon S3, the cloud-basedstorage system 318 may include one or more modules (e.g., a module ofcomputer program instructions executing on an EC2 instance) that areconfigured to ensure that when the local storage of a particular cloudcomputing instance is rehydrated with data from S3, the appropriate datais actually in S3. This issue arises largely because S3 implements aneventual consistency model where, when overwriting an existing object,reads of the object will eventually (but not necessarily immediately)become consistent and will eventually (but not necessarily immediately)return the overwritten version of the object. To address this issue, insome embodiments of the present disclosure, objects in S3 are neveroverwritten. Instead, a traditional ‘overwrite’ would result in thecreation of the new object (that includes the updated version of thedata) and the eventual deletion of the old object (that includes theprevious version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system 318 does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described above may beuseful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. AI applications may be deployed in a variety of fields,including: predictive maintenance in manufacturing and related fields,healthcare applications such as patient data & risk analytics, retailand marketing deployments (e.g., search advertising, social mediaadvertising), supply chains solutions, fintech solutions such asbusiness analytics & reporting tools, operational deployments such asreal-time analytics tools, application performance management tools, ITinfrastructure management tools, and many others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson, MicrosoftOxford, Google DeepMind, Baidu Minwa, and others. The storage systemsdescribed above may also be well suited to support other types ofapplications that are resource intensive such as, for example, machinelearning applications. Machine learning applications may perform varioustypes of data analysis to automate analytical model building. Usingalgorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism. Readers will appreciate thatin embodiments where the storage systems are embodied as cloud-basedstorage systems as described below, virtual drive or other componentswithin such a cloud-based storage system may also be configured

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. As part of that process, semi-structured andunstructured data such as, for example, internet clickstream data, webserver logs, social media content, text from customer emails and surveyresponses, mobile-phone call-detail records, IoT sensor data, and otherdata may be converted to a structured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

For further explanation, FIG. 5 illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 5 , computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 5 , the components illustrated in FIG. 5 are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 5 will nowbe described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

FIG. 6A is a block diagram of a further embodiment of the storagecluster 160 of FIGS. 1-5 . In this embodiment, the components are in achassis 138, such as the chassis 138 with multiple slots shown in FIG. 1. A power supply 606, with a power distribution bus 172 (as seen in FIG.2 ), provides electrical power to the various components in the chassis138. Two storage nodes 150 are shown coupled to a pathway 604, such as anetwork switch 620 in one embodiment. Further pathways are readilydevised. The pathway 604 couples the storage nodes 150 to each other,and can also couple the storage nodes 150 to a network external to thechassis 138, allowing connection to external devices, systems ornetworks.

Multiple storage units 152 are coupled to each other and to the storagenodes 150 by another pathway 602, which is distinct from the networkswitch 620 or other pathway 604 coupling the storage nodes 150. In oneembodiment, the pathway 602 that couples the storage units 152 and thestorage nodes 150 is a PCI Express bus (PCIe), although other busses,networks and various further couplings could be used. In someembodiments, there is transparent bridging for the storage node 150 tocouple to the pathway 602, e.g., to the PCI Express bus.

In order to connect to the two pathways 602, 604, each storage node 150has two ports 608, 610. One of the ports 610 of each storage node 150couples to one of the pathways 604, the other port 608 of each storagenode 150 couples to the other pathway 602.

In some embodiments, each of the storage nodes 150 can perform computefunctions as a compute node. For example, a storage node 150 could runone or more applications. Further, the storage nodes 150 can communicatewith the storage units 152, via the pathway 602, to write and read userdata (e.g., using erasure coding) as described with reference to FIGS.1-3 above. As another example, a storage node 150 executing one or moreapplications could make use of the user data, generating user data forstorage in the storage units 152, reading and processing the user datafrom the storage units 152, etc. Even with loss of one of the storageunits 152, or in some embodiments, loss of two of the storage units 152,the storage nodes 150 and/or remaining storage units 152 can still readthe user data.

In some embodiments, the erasure coding functions are performed mostlyor entirely in the storage units 152, which frees up the computing powerof the storage nodes 150. This allows the storage nodes 150 to focusmore on compute node duties, such as executing one or more applications.In some embodiments, the erasure coding functions are performed mostlyor entirely in the storage nodes 150. This allows the storage nodes 150to focus more on storage node duties. In some embodiments, the erasurecoding functions are shared across the storage nodes 150 and the storageunits 152. This allows the storage nodes 150 to have available computingbandwidth shared between compute node duties and storage node duties.

With the two pathways 602, 604 being distinct from each other, severaladvantages become apparent. Neither pathway 602, 604 becomes abottleneck, as might happen if there were only one pathway coupling thestorage nodes 150 and the storage units 152 to each other and to anexternal network. With only one pathway, a hostile could gain directaccess to the storage units 152 without having to go through a storagenode 150. With two pathways 602, 604, the storage nodes 150 can coupleto each other through one pathway 604, e.g., for multiprocessingapplications or for inter-processor communication. The other pathway 602can be used by either of the storage nodes 150 for data access in thestorage units 152. The architecture shown in FIG. 6A thus supportsvarious storage and computing functions and scenarios. Particularly, oneembodiment as shown in FIG. 6A is a storage and computing system in asingle chassis 138. Processing power, in the form of one or more storagenodes 150, and storage capacity, in the form of one or more storageunits 152, can be added readily to the chassis 138 as storage and/orcomputing needs change.

FIG. 6B is a variation of the storage cluster 160 of FIG. 6A. In thisversion, the pathway 612 has portions specific to storage units 152included in each storage node 150. In one embodiment, the pathway 612 isimplemented as a PCI Express bus coupling together storage units 152 andthe storage node 150. That is, the storage node 150 and storage units152 in one blade share a PCI Express bus in some embodiments. The PCIExpress bus is specific to the blade, and is not coupled directly to thePCI Express bus of another blade. Accordingly, storage units 152 in ablade can communicate with each other and with the storage node 150 inthat blade. Communication from a storage unit 152 or a storage node 150in one blade to a storage node 150 or storage unit 152 in another bladeoccurs via the network switch 620, e.g., the pathway 614.

FIG. 7 is a block diagram of a further embodiment of the storage cluster160 of FIGS. 1-5 , suitable for data storage or a combination of datastorage and computing. The version of FIG. 7 has all of the storageunits 152 coupled together by a first pathway 616, which could be a bus,a network or a hardwired mesh, among other possibilities. One storagenode 150 is coupled to each of two storage units 152. Another storageunit 152 is coupled to each of two further storage units 152. Thecoupling from the storage nodes 150 to storage units 152 illustrates asecond pathway 618.

FIG. 8A is a block diagram of a further embodiment of the storagecluster 160 of FIGS. 1-5 , with switches 620. One switch 620 couples allof the storage nodes 150 to each other. Another switch 620 also couplesall of the storage nodes 150 together. In this embodiment, each storagenode 150 has two ports, with each port connecting to one of the switches620. This arrangement of ports and switches 620 provides two paths foreach storage node 150 to connect to any other storage node 150. Forexample, the left-most storage node 150 can connect to the rightmoststorage node 150 (or any other storage node 150 in the storage cluster160) via a choice of either the first switch 620 or the second switch620. It should be appreciated that this architecture relievescommunication bottlenecks. Further embodiments with one switch 620, twoswitches 620 coupled to each other, or more than two switches 620, andother numbers of ports, or networks, are readily devised in keeping withthe teachings herein.

FIG. 8B is a variation of the storage cluster 160 of FIG. 8A, with theswitches 620 coupling the storage units 152. As in the embodiment inFIG. 8A, the switches 620 couple the storage nodes 150, providing twopaths for each storage node 150 to communicate with any other storagenode 150. In addition, the switches 620 couple the storage units 152.Two of the storage units 152 in each storage node 150 couple to one ofthe switches 620, and one or more of the storage units 152 in eachstorage node 150 couple to another one of the switches 620. In thismanner, each storage unit 152 can connect to roughly half of the otherstorage units 152 in the storage cluster via one of the switches 620. Ina variation, the switches 620 are coupled to each other (as shown in thedashed line in FIG. 8B), and each storage unit can connect to any otherstorage unit 152 via the switches 620. Further embodiments with oneswitch 620, or other numbers of switches 620 and arrangements ofconnections and the number of components being connected are readilydevised in keeping with the teachings herein.

FIG. 9A is a block diagram of compute nodes 626 coupled together for thestorage cluster 160. A switch 620 couples all of the compute nodes 626together, so that each compute node 626 can communicate with any othercompute node 626 via the switch 620. In various embodiments, eachcompute node 626 could be a compute-only storage node 150 or aspecialized compute node 626. In the embodiment shown, the compute node626 has three processor complexes 628. Each processor complex 628 has aport 630, and may also have local memory and further support (e.g.,digital signal processing, direct memory access, various forms of I/O, agraphics accelerator, one or more processors, and so on). Each port 630is coupled to the switch 620. Thus, each processor complex 628 cancommunicate with each other processor complex 628 via the associatedport 630 and the switch 620, in this architecture. In some embodiments,each processor complex 628 issues a heartbeat (a regular communicationthat can be observed as an indicator of ongoing operation, with the lackof a heartbeat signaling a possible failure or unavailability of thecompute node or processor). In some embodiments, each compute node 626issues a heartbeat. Storage nodes 150 and/or storage units 152 alsoissue heartbeats, in further embodiments.

FIG. 9B is a block diagram of a further embodiment of the storagecluster 160 of FIGS. 1-5 , with the compute nodes 626 of FIG. 9A. Thisembodiment is also shown with storage nodes 150. A switch 620 couplesall ports of all of the storage nodes 150, all ports of all of thecompute nodes 626 (e.g., all processor complexes 628 of all of thecompute nodes 626), and all storage units 152. In variations, fewer ormore storage nodes 150, fewer or more compute nodes 626, fewer or morestorage units 152, and fewer or more processor complexes 628 could beinstalled in the chassis 138. Each storage node 150, storage unit 152,or compute node 626 could occupy one or more slots 142 (see FIG. 1 ) inthe chassis 138. It should be appreciated that FIGS. 9A and 9B are oneexample and not meant to be limiting. In some embodiments multipleswitches 620 may be integrated into chassis 138 and the compute nodes626 may be coupled to the multiple switches in order to achieve thecommunication flexibility provided by the embodiments described herein,similar to the embodiments of FIGS. 8A and 8B.

FIG. 9C is a block diagram of a variation of the storage cluster 160with compute nodes 626 of FIG. 9B, depicting storage nodes 150, storageunits 152 and compute nodes 626 in multiple chassis 138, all coupledtogether as one or more storage clusters 160. Several chassis 138 couldbe rack-mounted and coupled together in the manner depicted, forexpansion of a storage cluster 160. In this embodiment, the switch 620or switches 620 in each chassis 138 couple the components in the chassis138 as described above with reference to FIG. 9B, and the switch 620 orswitches 620 in all of the chassis 138 are coupled together across allof the chassis 138. With various combinations of storage nodes 150and/or compute nodes 626, storage capacity and/or compute capacity(e.g., for running applications, operating system(s), etc.) is readilyconfigured and expanded or contracted, or virtualized in virtualcomputing environments. The use of switches 620 decreases or eliminatesthe usual patch wiring seen in many other rack-mounted systems.

Some embodiments of this and other versions of the storage cluster 160can support two or more independent storage clusters, in one chassis138, two chassis 138, or more chassis 138. Each storage cluster 160 in amulti-storage cluster environment can have storage nodes 150, storageunits 152, and/or compute nodes 626 in one, another, or both or morechassis 138, in various combinations. For example, a first storagecluster 160 could have several storage nodes 150 in one chassis 138 andone or more storage nodes 150 in another chassis 138. A second storagecluster 160 could have one or more storage nodes 150 in the firstchassis 138 and one or more storage nodes 150 in the second chassis 138.Either of these storage clusters 160 could have compute nodes 626 ineither or both of the chassis 138. Each storage cluster 160 could haveits own operating system, and have its own applications executing,independently of the other storage cluster(s) 160.

FIG. 9D is a block diagram of an embodiment of a multi-chassis storagecluster 160, depicting multilevel load-balancing. An aggregator switch904 is coupled through an array network 914 to multiple chassis 138,each of which has multiple blades 924 with storage nodes 150. Blades 924and storage nodes 150 can be heterogeneous in each chassis 138 as wellas across chassis 138 of the storage cluster 160, in type of blade, typeof storage node, amount of storage memory and/or network bandwidth.Inside each chassis 138, a chassis network 922 couples to the blades 924and storage nodes 150 in various versions, and need not be the same inall of the chassis 138.

A client 902 sees a single MAC (media access control) address 910 forthe entire, multi-chassis storage cluster 160 in this embodiment. For ascenario of a packet from a client 902 addressed to the storage cluster160, the packet arrives at the aggregator switch 904 that has the MACaddress 910. A load balancer 908 adjusts load-balancing according to thechassis weights 912 of the multiple chassis 138 coupled to the arraynetwork 914. These chassis weights 912 are determined by the aggregatorswitch 904, for example using a processor, based on communication withthe chassis 138 and the numbers of blades, storage nodes, andcommunication bandwidths of the chassis, blades and storage nodes, invarious embodiments. For example, one of the chassis 138 could have moreblades or storage nodes than another chassis, and have a higher chassisweight 912 assigned, while another chassis with fewer blades or storagenodes has a lower chassis weight 912. A chassis 138 that has blades ofhigher network bandwidth (e.g., more of the 50 G blades) could have ahigher chassis weight 912 than another chassis 138 that has blades oflower network bandwidth (e.g. more of the 20 G blades). The loadbalancer 908 determines to which chassis 138 to send the packet, andsends the packet through the switches of the external fabric module 906,and the array network 914, to the selected chassis 138.

Each chassis presents a single MAC address 926, and it is in accordancewith this MAC address 926 that the packet is sent to the selectedchassis 138. After that chassis 138 (for example, in this scenario,chassis 1), a load balancer 918 determines to which blade 924 or storagenode 150 to send the packet, and sends the packet through the switchesof the fabric module 916 internal to the chassis 138, and through thechassis network 922, to the selected blade 924 or storage node 150. Insome embodiments, the load balancer 918 does so through consultationwith a table 920.

Through the multilevel load-balancing, a chassis 138 with more blades924 or storage nodes 150, or greater network bandwidth from having morehigher bandwidth blades or storage nodes, receives proportionally morepackets. A 138 with fewer blades 924 or storage nodes 150, or lessnetwork bandwidth, receives proportionally fewer packets. By having thearray network 914 coupling the aggregator switch 904 and the multiplechassis 138, and the chassis networks 922 inside the chassis 138, themulti-chassis storage cluster 160 avoids network bottlenecks andefficiently sends packets to blades 924 and storage nodes 150, withload-balancing at the aggregator switch 904 and further load-balancingat each chassis 138.

Multiple features are evident in some or all of the embodiments shown inFIGS. 6A-9D. Many embodiments provide a pathway such that each storageunit 152 can communicate directly with one or more other storage units152 on such a pathway without assistance from any storage node 150. Thatis, a storage unit 152 can communicate with another storage unit 152,via a pathway, with storage nodes 150 being non-participatory in suchcommunication. No storage node 150 intervenes in or assistscommunication via this direct pathway from one storage unit 152 toanother storage unit 152. Some embodiments provide such a direct pathwayfor any communication from any storage unit 152 to any other storageunit 152. Some embodiments provide such a direct pathway forcommunication from each storage unit 152 to one or multiple otherstorage units 152, but not necessarily to all other storage units 152.In these cases, a storage unit 152 could communicate with anotherstorage unit 152 via one or more of the storage nodes 150 and anotherpathway, i.e., with assistance from a storage node 150.

In some embodiments, a pathway for direct communication from one storageunit 152 to any other storage unit 152 is included in couplings of othercomponents of the storage cluster 160. In some embodiments, each storagenode 150 can communicate directly with each storage unit 152 in theentire storage cluster 160. In some embodiments, each storage node 150can communicate with some of the storage units 152 directly, andcommunicate with other storage units 152 via another storage node 150.In some embodiments, the pathways for communication among storage nodes150 and communication among storage units 152 are separated, in othersthese pathways are combined. In some embodiments, the pathways forcommunication between storage nodes 150 and storage units 152, andcommunication among storage units 152 are separated, and in others thesepathways are combined.

One version of the storage node 150 has two ports 608, 610. Both ports608, 610 are employed for communication to other storage nodes 150 via achoice of two different pathways, in some embodiments. One port 610 isemployed for communication to other storage nodes 150 via one pathway,and another port 608 is employed for communication with storage units152 via another pathway, in some embodiments. Both ports 608, 610 areemployed for communication to storage nodes 150 and storage units 152,in some embodiments. By supporting direct communication among storageunits 152, these various architectures can reduce communicationbottlenecks. Storage nodes 150, and the processing and communicationbandwidths are not tied up in supporting the communication among thestorage units 152. As a result of this offloading, storage nodes 150 forfaster operations on user data, or these functions can be transferred tothe storage units 152.

Communications among storage units 152 can include data, metadata,messages to make sure storage units 152 are alive, health and/or statusinformation, etc. With storage units 152 communicating directly withother storage units 152, without a storage node 150 (or processor orcontroller of a storage node 150) intervening, the storage node 150 isfree to manage other processes. Communication between storage nodes 150and storage units 152, or among storage units 152 when these take oversome of the storage node 150 functions, can include data shards, withdata, metadata (e.g., information about and associated with the data)and metametadata (e.g., metadata about the metadata). Such communicationcan also include parity shards, health, status and performanceinformation. By making storage units 152 accessible by other storageunits 152 or by storage nodes 150 (e.g., processors of storage nodes150), the distinction of data ownership can be shifted to varyingdegrees from storage node 150 to storage units 152. This could involveshifting authorities 168 or wards among storage nodes 150 and storageunits 152 in various ways in some embodiments.

With a storage unit 152 on a network, a storage unit 152 couldcommunicate directly with a compute node 626. Such communication couldinvolve embedding a compute node identifier into a request and havingthe storage unit 152 directly return data to the compute node 626instead of returning data to a storage node 150 and then to the computenode 626. Direct connections for data, and data caching could be enabledfor a compute node 626 which has the intelligence to find data instorage units 152. Compute nodes 626 could also be used for specializedprocessing in a data pipeline implementing filtering, transformations,etc., for data going to or coming from storage units 152. Thearchitectures disclosed in FIGS. 6A-9D thus show flexibility forarrangement of components and communication among the components instorage systems and storage and computing systems. Depending upon datathroughput and communication throughput, and absolute or relativeamounts of data and compute function needs and projected growth, onearchitecture may be more suitable than another. Storage capacity andcompute capacity are adjustable, expandable and scalable, in variousembodiments. In addition, the embodiments provide more flexibility forload balancing.

Storage clusters 160, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 160. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 are required to cooperate to store andretrieve the data. Storage memory or storage devices, as used in storagearrays in general, are less involved with processing and manipulatingthe data. Storage memory or storage devices in a storage array receivecommands to read, write, or erase data. The storage memory or storagedevices in a storage array are not aware of a larger system in whichthey are embedded, or what the data means. Storage memory or storagedevices in storage arrays can include various types of storage memory,such as RAM, solid state drives, hard disk drives, etc. The storageunits 152 described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 160, as described herein, multiple controllers inmultiple storage units 152 and/or storage nodes 150 cooperate in variousways (e.g., for erasure coding, data sharding, metadata communicationand redundancy, storage capacity expansion or contraction, datarecovery, and so on).

FIG. 10A is a flow diagram of a method for operating a storage cluster,which can be practiced on or by embodiments of the storage cluster,storage nodes and/or non-volatile solid state storages or storage unitsin accordance with some embodiments. In an action 1002, a first storageunit receives a direction regarding metadata or a portion of user data,from a storage node of a storage cluster. For example, the directioncould include a direction to store a portion of user data or a datashard, read a portion of user data or a data shard, construct data fromdata shards, read or write a parity shard, a direction to respond abouthealth, status or performance, etc.

In an action 1004, the first storage unit communicates directly with asecond storage unit via a pathway that does not require assistance fromany storage node or storage nodes. This communication could involvecommunicating about the metadata or the portion of user data. A suitableexample of communication about the metadata is communication of aheartbeat (which relates to the direction to respond about health,status or performance). Examples of communication about the portion ofthe user data would be to request a data shard from another storageunit, or to send a parity shard to another storage unit for writing intoflash memory of that storage unit. Further examples are readily devisedin keeping with the teachings herein. In an action 1006, the secondstorage unit receives the communication from the first storage unit, viathe pathway. More specifically, the second storage unit receives thecommunication directly from the first storage unit, not from a storagenode.

In an action 1008, the second storage unit determines an action, basedon the communication from the first storage unit. Depending on contentof the communication, the second storage unit could store data, storemetadata, read data or metadata and send it back to the first storageunit, respond to an inquiry from the first storage unit, and so on. Aresponse, where appropriate, could be sent from the second storage unitback to the first storage unit, or to another storage unit, via apathway that does not require assistance from any storage node orstorage nodes. Or, the action could be for the second storage unit tocommunicate with one of the storage nodes, or a compute node. Furtherexamples of actions are readily devised in keeping with the teachingsherein.

FIG. 10B is a flow diagram of a method of load-balancing for a storagesystem. The method can be practiced by embodiments of the multi-chassisstorage cluster with multilevel load-balancing, and various embodimentsof storage systems described herein. In an action 1010, an I/O requestis received at a switch. For example, this could be the aggregatorswitch that is coupled through an array network to multiple chassis, asshown in FIG. 9D. In an action 1012, the aggregator switch determines towhich chassis to send the I/O request, based on a first load-balancingmechanism. In an action 1014, the I/O request is forwarded from theswitch to a fabric module of the determined chassis. In someembodiments, each chassis presents a MAC address, which is used for suchforwarding. The forwarding is through the array network, in someembodiments.

In an action 1016, the fabric module of the chassis determines to whichblade in the chassis to send the I/O request, based on a secondload-balancing mechanism. For example, in the multilevel load-balancingdescribed with reference to FIG. 9D, the first load-balancing mechanismis in the aggregator switch and the second load-balancing mechanism isin each chassis, at the fabric module. In an action 1018, the I/Orequest is forwarded from the fabric module of the chassis to thedetermined blade. This forwarding is through the chassis network, insome embodiments.

It should be appreciated that the methods described herein may beperformed with a digital processing system, such as a conventional,general-purpose computer system. Special purpose computers, which aredesigned or programmed to perform only one function may be used in thealternative. FIG. 11 is an illustration showing an exemplary computingdevice which may implement the embodiments described herein. Thecomputing device of FIG. 11 may be used to perform embodiments of thefunctionality for a storage node or a non-volatile solid state storageunit in accordance with some embodiments. The computing device includesa central processing unit (CPU) 1101, which is coupled through a bus1105 to a memory 1103, and mass storage device 1107. Mass storage device1107 represents a persistent data storage device such as a disc drive,which may be local or remote in some embodiments. The mass storagedevice 1107 could implement a backup storage, in some embodiments.Memory 1103 may include read only memory, random access memory, etc.Applications resident on the computing device may be stored on oraccessed via a computer readable medium such as memory 1103 or massstorage device 1107 in some embodiments. Applications may also be in theform of modulated electronic signals modulated accessed via a networkmodem or other network interface of the computing device. It should beappreciated that CPU 1101 may be embodied in a general-purposeprocessor, a special purpose processor, or a specially programmed logicdevice in some embodiments.

Display 1111 is in communication with CPU 1101, memory 1103, and massstorage device 1107, through bus 1105. Display 1111 is configured todisplay any visualization tools or reports associated with the systemdescribed herein. Input/output device 1109 is coupled to bus 505 inorder to communicate information in command selections to CPU 1101. Itshould be appreciated that data to and from external devices may becommunicated through the input/output device 1109. CPU 1101 can bedefined to execute the functionality described herein to enable thefunctionality described with reference to FIGS. 1-6 . The code embodyingthis functionality may be stored within memory 1103 or mass storagedevice 1107 for execution by a processor such as CPU 1101 in someembodiments. The operating system on the computing device may beMS-WINDOWS™, UNIX™, LINUX™, iOS™, CentOS™, Android™, Redhat Linux™,z/OS™, or other known operating systems. It should be appreciated thatthe embodiments described herein may be integrated with virtualizedcomputing system also.

Detailed illustrative embodiments are disclosed herein. However,specific functional details disclosed herein are merely representativefor purposes of describing embodiments. Embodiments may, however, beembodied in many alternate forms and should not be construed as limitedto only the embodiments set forth herein.

It should be understood that although the terms first, second, etc. maybe used herein to describe various steps or calculations, these steps orcalculations should not be limited by these terms. These terms are onlyused to distinguish one step or calculation from another. For example, afirst calculation could be termed a second calculation, and, similarly,a second step could be termed a first step, without departing from thescope of this disclosure. As used herein, the term “and/or” and the “/”symbol includes any and all combinations of one or more of theassociated listed items.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, and/or “including”, when used herein, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Therefore, the terminology usedherein is for the purpose of describing particular embodiments only andis not intended to be limiting.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

With the above embodiments in mind, it should be understood that theembodiments might employ various computer-implemented operationsinvolving data stored in computer systems. These operations are thoserequiring physical manipulation of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. Further, the manipulationsperformed are often referred to in terms, such as producing,identifying, determining, or comparing. Any of the operations describedherein that form part of the embodiments are useful machine operations.The embodiments also relate to a device or an apparatus for performingthese operations. The apparatus can be specially constructed for therequired purpose, or the apparatus can be a general-purpose computerselectively activated or configured by a computer program stored in thecomputer. In particular, various general-purpose machines can be usedwith computer programs written in accordance with the teachings herein,or it may be more convenient to construct a more specialized apparatusto perform the required operations.

A module, an application, a layer, an agent or other method-operableentity could be implemented as hardware, firmware, or a processorexecuting software, or combinations thereof. It should be appreciatedthat, where a software-based embodiment is disclosed herein, thesoftware can be embodied in a physical machine such as a controller. Forexample, a controller could include a first module and a second module.A controller could be configured to perform various actions, e.g., of amethod, an application, a layer or an agent.

The embodiments can also be embodied as computer readable code on anon-transitory computer readable medium. The computer readable medium isany data storage device that can store data, which can be thereafterread by a computer system. Examples of the computer readable mediuminclude hard drives, network attached storage (NAS), read-only memory,random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and otheroptical and non-optical data storage devices. The computer readablemedium can also be distributed over a network coupled computer system sothat the computer readable code is stored and executed in a distributedfashion. Embodiments described herein may be practiced with variouscomputer system configurations including hand-held devices, tablets,microprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers and the like. Theembodiments can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a wire-based or wireless network.

Although the method operations were described in a specific order, itshould be understood that other operations may be performed in betweendescribed operations, described operations may be adjusted so that theyoccur at slightly different times or the described operations may bedistributed in a system which allows the occurrence of the processingoperations at various intervals associated with the processing.

In various embodiments, one or more portions of the methods andmechanisms described herein may form part of a cloud-computingenvironment. In such embodiments, resources may be provided over theInternet as services according to one or more various models. Suchmodels may include Infrastructure as a Service (IaaS), Platform as aService (PaaS), and Software as a Service (SaaS). In IaaS, computerinfrastructure is delivered as a service. In such a case, the computingequipment is generally owned and operated by the service provider. Inthe PaaS model, software tools and underlying equipment used bydevelopers to develop software solutions may be provided as a serviceand hosted by the service provider. SaaS typically includes a serviceprovider licensing software as a service on demand. The service providermay host the software, or may deploy the software to a customer for agiven period of time. Numerous combinations of the above models arepossible and are contemplated.

Various units, circuits, or other components may be described or claimedas “configured to” perform a task or tasks. In such contexts, the phrase“configured to” is used to connote structure by indicating that theunits/circuits/components include structure (e.g., circuitry) thatperforms the task or tasks during operation. As such, theunit/circuit/component can be said to be configured to perform the taskeven when the specified unit/circuit/component is not currentlyoperational (e.g., is not on). The units/circuits/components used withthe “configured to” language include hardware—for example, circuits,memory storing program instructions executable to implement theoperation, etc. Reciting that a unit/circuit/component is “configuredto” perform one or more tasks is expressly intended not to invoke 35U.S.C. 112, sixth paragraph, for that unit/circuit/component.Additionally, “configured to” can include generic structure (e.g.,generic circuitry) that is manipulated by software and/or firmware(e.g., an FPGA or a general-purpose processor executing software) tooperate in manner that is capable of performing the task(s) at issue.“Configured to” may also include adapting a manufacturing process (e.g.,a semiconductor fabrication facility) to fabricate devices (e.g.,integrated circuits) that are adapted to implement or perform one ormore tasks.

The foregoing description, for the purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the embodiments and its practical applications, to therebyenable others skilled in the art to best utilize the embodiments andvarious modifications as may be suited to the particular usecontemplated. Accordingly, the present embodiments are to be consideredas illustrative and not restrictive, and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

With reference to FIGS. 12-16B, scale-out storage platforms, along withtechnological problems and solutions arising therein, are described. Thevarious mechanisms and components can be implemented in, or with,embodiments of storage systems described above with reference to FIGS.1A-5 , variations thereof, and various further storage systems.

FIG. 12 illustrates a deployment model for a storage system inaccordance with some embodiments of the present disclosure. Blades 1216numbered 1-N are connected to two fabric modules 1212, 1214 labeled FM-1and FM-2 respectively. The fabric modules 1212, 1214 are connected totwo top of rack switches 1202, 1206 labeled TOR-1 and TOR-2,respectively. Ports of each blade 1216, in one example labeled ir0 andir1, are grouped and configured as a blade port channel 1220 for thatblade 1216. One port of the blade 1216 is connected via a link 1218 toone of the fabric modules 1212, and another part of the blade 1216 isconnected by another link to the other fabric module 1214. Networkcommunication from a blade 1216 to the fabric modules 1212, 1214 can beload balanced across the ports ir0 and ir1 of the blade port channel1220 and associated links 1218, or left unbalanced, depending onimplementation and/or circumstances, in various embodiments.

Ports and associated links connecting the fabric modules 1212, 1214 andthe top of rack switches 1202, 1206 are grouped and configured as amulti-switch link aggregation group (LAG), forming a single uplink portchannel. The two top of rack switches 1202, 1206 are connected to eachother through an inter-switch link (ISL) 1204.

In FIG. 12 deployment model, the storage system makes use ofconnectivity to a customer's uplink switches to use multi-linkaggregation (MLAG, which may also refer to multi-chassis linkaggregation group, and abbreviated as MCLAG or MC-LAG). MLAG enables apair of Fabric Modules (FMs) to be presented as a single switchingdevice. The connectivity between the FMs and blades is also over theMLAG. It should be appreciated that this architecture may be thepreferred mode of bandwidth aggregation since this storage systemarchitecture is a scale out storage device and both fabric modules areactively serving I/Os.

It should be further appreciated that the majority of the enterprisestorage arrays/solutions are typically based on active/secondarycontroller architecture. The network architecture and solutions arebuilt keeping the active/secondary model. High-availability in networkconnectivity is achieved via Active/Passive Link Aggregation.

The storage system embodiment in FIG. 12 offers active-active, scale-outstorage. This architecture requires customers to deploy active-activeMulti-Switch Link aggregation. However, to install this storage systemembodiment of FIG. 12 , or replace legacy storage with otheractive-active system architectures, customers may need to re-design orreconfigure their network architecture. This requirement is generallymet with high resistance and requires close collaboration betweennetwork and storage administrators.

FIG. 13 illustrates an active/passive link aggregation group (LAG) in astorage system embodiment. Blades 1318 numbered 1-N are connected to twofabric modules 1310, 1312 labeled EFM-1 and EFM-2 (external fabricmodule, in this embodiment) via links 1320 grouped as blade portchannels 1316, similar to the embodiment shown in FIG. 12 . One externalfabric module 1310, EFM-1, is connected to a switch 1302 designated theactive switch, in a customer legacy network. The other external fabricmodule 1312, EFM-2, is connected to a switch 1304 designated the passiveswitch, in the customer legacy network. The fabric modules 1310, 1312are connected to each other with an ISL 1314. Ports and links connectingthe fabric module 1310 EFM-1 and the active switch 1302 are grouped andconfigured as an active LAG 1306. Ports and links connecting the fabricmodule 1312 EFM-2 and the passive switch 1304 are grouped and configuredas a passive LAG 1308. It should be appreciated that in the legacy mode,upstream switches are configured in Active Mode or Passive Mode. In thisconfiguration, only links from the Active switch will be operational.Active/Passive links are connected to the storage system, morespecifically to the fabric modules, as shown in FIG. 13 .

Still referring to FIG. 13 , blades 1318 send traffic to both EFMs,i.e., the two fabric modules 1310, 1312. Fabric module 1312 EFM-2forwards network traffic to fabric module 1310 EFM-1 via ISLs 1314 tosend the traffic back to the customer network, since the passive switch1304 is not active (but is available for failover in case the activeswitch 1302 goes offline).

The legacy Active/Passive architecture for the legacy customer networkand the storage system embodiment in FIGS. 12 and 13 may be improved asdescribed herein. The ISL 1314 between EFMs, i.e., fabric modules 1310,1312, become the bottleneck as EFM-2 needs to pass traffic to EFM-1 forsending back to the customer network. In a more optimal solution, bothEFM-1 and EFM-2 should be able to participate in processing clientnetwork traffic without dependency on ISL 1314. Any failure event in thenetwork should have minimum impact on the internal networking of thestorage system and should auto-adjust to the new configuration.

FIG. 14 illustrates a multi-chassis link aggregation group (MLAG) in astorage system embodiment. Blades 1420 numbered 1-N are connected to twofabric modules 1414, 1416 labeled EFM-1 and EFM-2 (for external fabricmodule, in this embodiment) via links grouped as blade port channels1418, similar to the embodiments shown in FIGS. 12 and 13 . One fabricmodule 1414, EFM-1, is connected to both a switch 1402 designated theactive switch, and a switch 1404 designated the passive switch, in acustomer legacy network. The other fabric module 1416, EFM-2, isconnected to both the switch 1402 designated the active switch, and theswitch 1404 designated the passive switch, in the customer legacynetwork. Equivalently, each of the active switch 1402 and the passiveswitch 1404 is connected to both of the fabric modules 1414, 1416, andvice versa. The fabric modules 1414, 1416 are connected to each otherwith an ISL 1412. Ports and links connecting active switch 1402 to thefabric module 1414 EFM-1 and the fabric module 1416 EFM-2 are groupedand configured as an active LAG 1406. The two links that connect thefirst fabric module 1414 and the first, active switch 1402 have higherrelative priority. The two links that connect the second fabric module1416 and the first, active switch 1402 have higher relative priority.Ports and links connecting passive switch 1404 to the fabric module 1414EFM-1 and the fabric module 1416 EFM-2 are grouped and configured as apassive LAG 1408. The two links that connect the first fabric module1414 and the second, passive switch 1404 have lower relative priority.The two links that connect the second fabric module 1414 and the second,passive switch 1404 have lower relative priority. All of the fabricmodule 1414, 1416 ports and links connecting the two fabric modules1414, 1416 to the active switch 1402 and the passive switch 1404 aregrouped together and configured as MLAG 1410.

In one embodiment of FIG. 14 , each customer switch 1402, 1404 will havea properly configured 802.3ad Link Aggregation Control Protocol (LACP)defined for the ports coming from the storage system, more specificallythe ports from the fabric modules 1414, 1416. Both Active and Passiveswitches 1402, 1404 are connected to both the EFMs, i.e., fabric modules1414, 1416 as shown in FIG. 14 . A customer's switches, e.g., customerlegacy network active switch 1402 and passive switch 1404, do not needto be MLAG, i.e. the port-channels on them may be independent. 802.3adLACP configuration on the Active switch 1402 will send a higher priorityvalue compared to the priority configured on the Passive switch 1404.Each customer switch 1402, 1404 will have a properly configured 802.3adLACP (Port-Channel) defined for the ports coming from the storagesystem, more specifically the ports from the fabric modules 1414, 1416.Both Active and Passive switches 1402, 1404 are connected or coupled toboth the EFMs as shown in FIG. 14 , mitigating the bottleneck at the ISL1412 between the EFMs. The storage system in FIG. 14 operates as if ithas a single LACP group to both switches 1402, 1404. In addition, thestorage system, more specifically the fabric modules 1414, 1416, selectsactive links based on relative priority. Consequently in someembodiments, only links connected to active switch 1402 will beoperational.

Continuing with FIG. 14 , in case of failure, when all links connectedto Active switch 1402 go offline; the storage system, more specificallythe fabric modules 1414, 1416, will switch to links configured withlower priority. These lower priority links are connected or coupled tothe previously passive switch 1404, which has now become primary due tothe failure event. That is, the storage system will failover to lowerpriority links and the passive switch 1404, which becomes active to takeover switching duties from the formerly active switch 1402. The systemconfigures for failover to links that have lower priority and areconnected to the second, passive switch 1404, when the first, activeswitch 1402 goes offline and the second, passive switch 1404 thus comesonline, i.e., is the active switch.

It should be appreciated that failback, e.g., returning to using aformerly failed but now recovered component, can happen if any of theoffline links with higher priority connected to the switch 1402 becomesoperational. Alternatively, a failback policy can be set to enable linksonly if N>=1 number of links have become operational. That is, a nonzeronumber of links can be specified as a threshold of operational links, tocause failback in some embodiments. The system configures for failbackfrom network communication through the second switch to networkcommunication through the first switch, when the specified nonzeronumber of offline links that have higher priority and are connected tothe first switch become operational.

It should be appreciated that in the various embodiments described withreference to FIG. 14 , the customer does not need to reconfigure theswitches 1402, 1404 of the customer legacy network. These storage systemembodiments thus solve a technological problem of how to install astorage system with active/active configuration and connect to acustomer legacy network that has active/passive configuration, withoutrequiring reconfiguration of the customer legacy network.

FIG. 15 illustrates a balancing process 1514 using virtual media accesscontrol (MAC) addresses 1504, 1510, as applicable to embodiments ofstorage systems which may be integrated into the embodiments. Thebalancing process 1514 can be implemented in software executing on oneor more processors 1512, firmware, hardware, or combinations thereof invarious embodiments. The processor(s) could be a processing device inone or more fabric modules 1414, 1416, or one or more blades 1420 inFIG. 14 in various embodiments. The balancing process 1514 can beimplemented as a localized process, a distributed process, or avirtualized process in various embodiments.

A technological problem that is addressed by these embodiments is thatport traffic 1516 through various ports 1502 of fabric modules 1414,1416 and/or blades 1420 with reference to FIG. 14 , may be unbalancedunder various circumstances. Data throughput for the storage systemdepends on data throughput for all of the ports 1502. If one or more ofthe ports 1502 is underutilized, overall data throughput for the storagesystem is reduced in some embodiments. The balancing process 1514attempts to even out the utilization of each of the ports 1502, tomaximize data throughput for the storage system.

One balancing process 1514 relies on hashing some portion of header ordata in a data packet for a TCP/IP connection, and determining whichport 1502 the data packet is routed through based on the hash value.More specifically, the hash value is correlated to an (InternetProtocol) IP address or a MAC address, which then points to a specificport 1502, and results in a pseudorandom distribution of data packetsacross ports 1502. However, because no hashing function will giveperfectly even distribution at all times for all data packets and allsets of IP addresses or MAC addresses for all ports 1502, the situationmay arise that a particular set of IP addresses or MAC addresses givesuneven distribution across the ports 1502, uneven data throughput acrossthe ports, and reduced or suboptimal data throughput for the storagesystem.

A solution to this problem could involve changing hash function,changing IP address, or changing MAC address, i.e., changing the“ingredients” for the pseudorandom distribution of data packets acrossports 1502. Changing the hash function is possible, in one embodiment,but less desirable to incorporate. In some embodiments of customerlegacy networks, the IP addresses cannot be changed because the customerhas already configured the network and more specifically the IPaddresses, and changing the IP addresses for the ports 1502 underconsideration will break the TCP/IP connection. In some embodiments, itis possible that on layer 2 of the protocol, the MAC address can bechanged, and doing so presents a solution that may be implemented intothe embodiments. The mechanism for changing MAC address of the ports1502 uses virtual MAC addresses, which can be rotated to see ifdistribution across the ports 1502 changes or more particularly resultsin improvement in balance of data distribution across the ports 1502during network communication, e.g., data I/O.

Continuing with reference to FIG. 15 , in one embodiment the ports 1502each have a virtual MAC address 1504 and switch statistics 1506 relatingto port traffic 1516 of the port 1502. For example, the processor(s)1512 write to a register or a table entry, etc., assigning a specificvirtual MAC address 1504 to the port 1502 in some embodiments. It shouldbe appreciated that the switch statistics 1506 record how many packetshave passed through the port 1502 since the last poll of an appropriateregister or table entry, etc., or since the last reset of the switchstatistics 1506.

The balancing process 1514 in FIG. 15 (see also FIG. 16B) accesses theswitch statistics 1506 of one or more of the ports 1502, and determineswhether the port traffic 1516 is balanced across the ports 1502. If theport traffic 1516 is not sufficiently balanced, e.g., does not achieve aspecified threshold, the balancing process 1514 changes (e.g., updates,writes) the virtual MAC address 1504 of one or more of the ports 1502,using the virtual MAC addresses 1510 in a table 1508. In this manner,the storage system can rotate through the virtual MAC addresses 1510 ofthe port(s) 1502, in an attempt to rebalance or more fully balance porttraffic 1516 across the ports 1502. This process can be iterative, withthe balancing process 1514 again determining whether port traffic 1516is balanced across the ports 1502, and if not, rebalancing as above. Forexample, the system could poll and optionally rebalance once per secondor other suitable interval, to maintain optimal data throughput balanceacross the ports 1502 and optimal storage system data throughput. Thismechanism can be applied to the ports of the fabric modules and/or theports of the blades, in various embodiments. It is significant that inthese embodiments and variations thereof, the customer does not need toreconfigure the switches in the customer legacy network. It should beappreciated that the embodiments may be extended to systems that utilizeand or communicate with virtual data IP addresses also referred to asdata VIPs. A storage vendor may assign multiple data VIPs to the storagesystem. The embodiments described herein become more precise and/or workmore efficiently as the number of data VIPs increases, as the change toone of many data VIPs in an orderly or random fashion results in findingthe best bandwidth/port distribution. In some embodiments, a relativelylarge number of data VIPs distributed across customer systems may beachieved via a domain name system round robin at mount time.

FIG. 16A illustrates a flow diagram of a method for a storage system,which is suitable for the storage system embodiment described herein.The method is performed by a processing device, such as one or moreprocessors in fabric modules and/or blades of a storage system,firmware, hardware or combinations thereof, in various embodiments. Theconfigurations are performed by the storage system and are consideredself-configurations of the system, which may be performed in response touser input or selection, in various embodiments.

In an action 1602, the storage system is connected to a customer legacynetwork. In one embodiment, the customer legacy network has a firstswitch that is designated active, and a second switch that is designatedpassive. The storage system has blades, a first fabric module and asecond fabric module. The connection could be part of an installation,replacement or upgrade process, and include various links amongcomponents, embodied in both physical connections and data connections.An example connection is illustrated above with reference to FIG. 14 .

In an action 1604 of FIG. 16A, connections and communication areconfigured in the storage system blades and fabric modules. For example,the storage system self-configures ports of the blades and fabricmodules, compliant with a specified protocol, so as to boot up anoperational storage cluster with connection and network communicationamong the blades and fabric modules of the storage system and theswitches of the customer legacy network.

In an action 1606, a first LAG (link aggregation group) is configured.The first LAG is configured active, and includes ports of a first,active switch of the customer legacy network, that connect via links tofirst and second fabric modules of the storage system. For example, thefirst and second fabric modules self-configure for network communicationhaving this first LAG. In an action 1608, a second LAG is configured.The second LAG is configured passive, and includes ports of a second,passive switch of the customer legacy network, that connect via links tofirst and second fabric modules of the storage system. For example, thefirst and second fabric modules self-configure for network communicationhaving this second LAG.

Continuing with FIG. 16A, in an action 1610, a MLAG (multi-chassis linkaggregation group, or multi-link aggregation group, which may also bereferred to by the acronym MCLAG or MC-LAG) is configured. The MLAGincludes ports of the first and second fabric modules that connect orcouple via links to the first, active switch and second, passive switchof the customer legacy network. For example, the first and second fabricmodules self-configure for network communication having this MLAG.

Through performance of these actions 1602, 1604, 1606, 1608, 1610, thestorage system provides an active/active configuration storage systemconnected to an active/passive configuration customer legacy network,without requiring reconfiguration of the customer network. This methodthereby provides improvement in storage system performance in comparisonto an active/passive configuration storage system connected to anactive/passive configuration customer legacy network, and providesimprovement in storage system performance in comparison to someembodiments of an active/active configuration storage system connectedto an active/passive configuration customer legacy network as describedabove.

FIG. 16B illustrates a flow diagram of a method for a storage system,which is suitable for the storage system embodiments in FIGS. 12-14 andfurther storage systems, and uses the balancing process depicted in FIG.15 . The method can be implemented in various storage system embodimentsthrough software executing on a processing device, firmware, hardware orcombinations thereof, for example in one or more fabric modules, one ormore blades, or distributed processing.

In an action 1620 of FIG. 16B, the system establishes one or more tablesof virtual MAC addresses. Each port is assigned a virtual MAC addressfrom the table(s), and the table(s) provide additional MAC addresses forrotation, pseudorandom selection or other assignment to ports. In anaction 1622, the system polls one or more ports, to monitor porttraffic. In some embodiments the ports could be ports of fabric modules,ports of blades, or both, in various embodiments. In one embodiment, thepolling includes reading switch statistics of the ports.

In a determination or decision action 1624, the system determineswhether the port traffic is balanced. This determination is based on thepolling and monitoring in the action 1622. If the answer is yes, theport traffic is balanced, the flow branches back to the action 1622, tocontinue monitoring port traffic. If the answer is no, the port trafficis unbalanced, and the flow proceeds to the action 1626.

In an action 1626, the system changes the virtual MAC address on one ormore ports. It should be appreciated that the ports could be the sameports that are polled, or different ports, or some of each, in variousembodiments. The action 1626 is responsive to determining the porttraffic is unbalanced, i.e., a “no” answer to the determination action1624. After changing virtual MAC address(es), flow branches back to theaction 1622, to continue monitoring port traffic. Through iteration ofthese actions, the system balances port traffic across the ports, andoptimizes data throughput across the ports and for the system overall.

It should be appreciated that the above embodiments may be generalizedfor variations with other numbers of fabric modules and switches,various protocols, and system-specific implementation components,connections and other details.

FIG. 17 illustrates a storage system 1710 that selects from amongmultiple hash algorithms 1720 and performs load-balancing according tothe selected hash algorithm, in accordance with an embodiment of thepresent disclosure. The storage system embodiments disclosed hereinimprove load-balancing in the storage system, by selecting a hashalgorithm that more closely meets one or more criteria 1718 forload-balancing than other hash algorithms. It should be appreciated thatmany factors could affect evenness of load-balancing, such as differentIP addresses, port addresses, data distribution, operating systems,communication protocols, routing algorithms, network changes and changesto storage system configuration. Such factors may differ among clientdevices 1702 and storage system configurations. The selected hashalgorithm is specific to network traffic 1708, so that using theselected hash algorithm for load-balancing optimizes load-balancingaccording to the criteria 1718, for that specific network traffic 1708pattern or circumstances.

In the scenario illustrated in FIG. 17 , multiple client devices 1702each have a source address 1704, and are sending network packets,collectively network traffic 1708 through a network 1706 (e.g., thecloud) to the storage system 1710. In the storage system, multipleblades or nodes 1722 each have a destination address 1724, and the loadbalancer 1714 operates to load balance the network traffic 1708 throughthe internal storage system network 1728 to the blades or nodes 1722.Criteria 1718 for optimizing load-balancing can be fixed or variable,system determined or user settable (e.g., by a system administrator orother user input), etc., in various embodiments. For example, at onetime the system may optimize load-balancing for even distribution ofnetwork traffic 1708 across the blades or nodes 1722. At another timethe system may optimize load-balancing for weighted distribution ofnetwork traffic 1708 favoring one or more specified blades or nodes1722, disfavoring one or more specified blades or nodes 1722, ordistribution-weighting a grouping of blades or nodes 1722. At one timethe system may load balance to balance or rebalance stored datadistribution across the storage memory 1726, directing load distributionof arriving data so as to change the balance of stored data across theblades or nodes 1722 in a targeted direction. The target or directionfor load-balancing, and the corresponding criteria 1718 for optimizingload-balancing, can change according to system circumstances and needs,in various embodiments. For example, installing or removing a blade ornode 1722 changes the number of blades or nodes 1722 in the storagesystem 1710 for load-balancing and could trigger changes to the criteria1718 or usage of the criteria 1718. A change in client, a change inclient data, a change in application(s) executing in the storage system1710 or other changes could trigger changes to the criteria 1718, orchanges in weighting of the criteria 1718 for the selector 1716, invarious embodiments.

Each blade or node 1722 in the storage system 1710 has a destinationaddress 1724, which is used in load-balancing. For example, thedestination address 1724 used in load-balancing could be a MAC address,IP address, or a port address. Likewise, each client device 1702 has asource address 1704, which could be for example a MAC address, IPaddress, or a port address. Devices generally may have more than one ofthese types of addresses (see, e.g., FIGS. 9A, 9D, 15-16B), yet use onlyone address type on a given network communication protocol layer and useonly one of these types of addresses for load-balancing. Storage memory1726 is depicted in FIG. 17 as generally across the blades or nodes1722, and could be distributed in heterogeneous or homogeneous amountsor types in blades or nodes 1722, in various embodiments, and may changeover time, e.g., with system upgrades, repair or replacement, or systemexpansion.

Various modules in the storage system 1710, such as the load balancer714 and selector 716, could be implemented in software executing on oneor more processors 1712, firmware, hardware or combinations thereof invarious embodiments. The processor(s) 1712 are depicted generally asbeing part of the storage system 1710, and could be implemented forexample as one or more blade or node processors, storage controllers,array controllers, centralized or distributed processing, virtualprocessing, etc. in various embodiments. In operation, the selector 1716selects from among multiple hash algorithms 1720 according to one ormore criteria 1718, and the load balancer 1714 uses the selected hashalgorithm for load-balancing the network traffic 1708 across the bladesor nodes 1722. In making the selection, the selector 1716 determinesthat the hash algorithm that is to be used across the network traffic1708 for load-balancing to the destination addresses 1724 in the storagesystem 1710 more closely meets one or more of the load-balancingcriteria 1718 than at least one other hash algorithm, in the set of hashalgorithms 1720. Depending on time, circumstances and system specificarchitecture, in some embodiments the selector 1716 selects the best oneof the hash algorithms 1720, i.e., the hash algorithm that most closelyof all of the hash algorithms 1720 meets the relevant load-balancingcriteria 1718. It should be appreciated that criteria 1718 could beimplemented as rules or policies, in various embodiments.

For example, one of the criteria 1718 is used to load-balance arrivingnetwork traffic 1708 evenly, i.e., for even distribution, across theblades or nodes 1722. Another one of the criteria 1718 is to distributearriving network traffic 1708 for best usage of heterogeneous amounts ofstorage memory 1726 across the blades or nodes 1722. Further criteria1718 could be used to distribute network traffic 1708 of a specified oneor more client devices 1702 (and associated source addresses 1704)across a specified group of blades or nodes 1722. Other criteria 1718could be to distribute commands across one set of destination addresses1724, distribute data across another set of destination addresses 1724,and/or distribute metadata across yet another set of destinationaddresses 1724. Further criteria 1718, and variations of the aboveexamples of criteria 718, are readily devised in keeping with theteachings herein for further embodiments.

The hash algorithms 1720 available in a given implementation of thestorage system 1710 may be of heterogeneous or homogeneous type.Generally, a hash algorithm is a pseudorandom generator, with inputacting as a seed, but hash algorithms may be of mathematical oralgorithmic types, and may be weighted or unweighted, and it may beadvantageous to have a variety of hash algorithms 1720 represented in anembodiment.

FIG. 18 illustrates usage of the selected hash algorithm inload-balancing, in accordance with an embodiment of the presentdisclosure. The modules and actions depicted in FIG. 18 could beimplemented as part of the load balancer 1714 in cooperation with theselector 1716, or as modules the load balancer 1714 and/or selector 1716cooperate with, in various embodiments. Network packets 1802 arrive atthe storage system 1710 in the network traffic 1708 and are operatedupon first to determine appropriate routing and second to actually routeto a destination address 1724.

In the embodiment depicted in FIG. 18 , the parameter extractor 1810operates on a network packet 1802, to extract one or more parametersthat will be applied to the selected hash algorithm 1812. For example,in some embodiments the hash algorithm operates on the source address1704 of the client device 1702 to determine packet routing, and theparameter extractor 1810 pulls the source address 1704 from the header1804 of the network packet 1802. In some embodiments, the hash algorithmoperates on aspects of data or metadata in the payload 1806 of thenetwork packet 1802, to determine packet routing, and the parameterextractor 1810 pulls a relevant portion of data or metadata from thepayload 1806. As a further example, both the source address 1704 and oneor more aspects of data or metadata in the header 1804 and/or thepayload 1806 may play a role in hash algorithm application, destinationaddress resolving and packet routing in load-balancing. As discussedabove, the criteria 1718 for load-balancing, and thus the emphasis onwhich parameter(s) to extract from a network packet 1802, may change intime for a given system, system configuration, and circumstances, or maybe fixed, in various embodiments.

The storage system 1710 applies the selected hash algorithm 1812 to theextracted parameter(s) from the network packet 1802, and produces thehash result 1814. A destination address resolver 1816 receives the hashresult 1814 and produces a destination address, for the packet routingmodule 1818. The packet routing module 1818 could, for example,substitute the determined destination address into the network packet1802, to resolve the LAG, MLAG or MCLAG destination address originallypresented in the header 1804 of the network packet 1802 into a specificdestination address 1724 of one of the blades or nodes 1722 of thestorage system 1710. Such address substitution is appropriate forsystems using various types of link aggregation groups (see, e.g., FIGS.12-14 and 16A), and implementation should conform to the appropriateprotocol. In this manner, the packet routing module 1818 operates on thenetwork packet 1802, and sends the network packet 1802 to theload-balanced destination. Variations on the above, for example forspecific network protocols, operating systems, single chassis andmulti-chassis systems, virtualized storage and computing systems, etc.,are readily devised for various embodiments.

In various embodiments, the selector 1716 and the load balancer 1714could each access and cooperate with the mechanisms shown and describedwith reference to FIG. 17 , or each could have its own private copy ofone or more such mechanisms, or a subset of these mechanisms, etc. Seealso examples of load balancers in FIG. 9D. For example, the selector1716 could test each of the available hash algorithms 1720 against aspecified range of parameters that the system determines are, orpredicts will be, available in network packets 1802 in the networktraffic 1708. The range of resolved addresses, e.g., from thedestination address resolver 1816, that results from such testing couldbe compared to the selected one or more criteria 1718 for load-balancingin an existing or upgraded storage system configuration, and suchcomparison informs and guides the selection of a hash algorithm 1720.The selector 1716 then makes the selected one of the hash algorithms1720 known or available to the load balancer 1714, which applies theselected hash algorithm 1812 to one or more extracted parameters fromeach network packet 1802, in load-balancing as described above.

In one embodiment, the selector 1716 uses machine learning, for examplethrough a database that is trained across one or more data sets, forselecting one of the hash algorithms 1720. In another embodiment, theselector 1716 uses artificial intelligence, for example through codedalgorithms or rule sets, for selecting one of the hash algorithms 1720.Pattern matching could be performed by a neural network, in someembodiments. A storage system could use benchmarking, snoop networkcommunication logs, look for traffic patterns, etc., and identify aparticular hash algorithm that matches or otherwise best suits arecognized traffic pattern. Generally speaking, the storage systemspecializes a hash algorithm to a particular environment of the storagesystem configuration, data storage circumstances, and network traffic,and obtains a better outcome for load-balancing that would be the casewith one, some, or all of the other hash algorithms 1720. Embodimentswith machine learning or artificial intelligence may learn andself-modify over time, for example recognizing network traffic patternsand storage system circumstances, for continued improvement inoptimizing load-balancing.

In some embodiments, the selection of one of the hash algorithms 1720,by the selector 1716, is performed during an administrative window,during a new connection, during an installation or an upgrade, orresponsive to one of these situations or events. It may be disruptive toperform such analysis and hash algorithm selection at other times,causing havoc on the network.

FIG. 19 is a flow diagram of a method performed by a storage system,which uses the hash algorithm selection depicted in FIGS. 17 and 18 orvariation thereof in accordance with an embodiment of the presentdisclosure. More specifically, the method is performed by a processingdevice, exemplified by the processor(s) 1712 in the storage system 1710in FIG. 17 . The method, or variation thereof, can be captured ininstructions in tangible, computer-readable media.

In an action 1902, the system determines source addresses, destinationaddresses and/or other parameters, for network traffic. The determinedparameter(s) can be extracted from network packets in network traffic,as described above with reference to FIG. 18 , and the determination ofwhich parameter(s) to extract from network packets may be system,configuration, and situation specific.

In an action 1904, the system determines a hash algorithm forload-balancing. The system selects a hash algorithm from a set ofmultiple hash algorithms that are available (and known in the system)for load-balancing. The hash algorithm more closely meets one or moreload-balancing criteria than one or more other hash algorithms in theset of hash algorithms, and is selected on that basis. Again, the choiceof which criteria to use for the determination of the hash algorithm maybe system, configuration and/or situation specific. Additionally, invarious embodiments, such determination may be to find one hashalgorithm that is better than one of the others, or find the best hashalgorithm in comparison to some or all of the others. That is, a hashalgorithm that more closely meets the relevant criteria 1718, accordingto present or predicted circumstances is provided through theembodiments described herein.

In an action 1906, the storage system distributes network traffic todestination addresses in the storage system, load balanced according tothe determined hash algorithm. Having gone through an analysis andselection process for the hash algorithms with awareness of networktraffic, the storage system thus optimizes storage system load-balancingthrough network specific hashing. It should be appreciated that theadaptive selection of various hash algorithms and the flexibilityenabled through the embodiments described herein may be applied to anyload balancing situation and is not limited to a storage systemapplication.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g. a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

1. A method performed by a storage system, comprising:

determining a plurality of source addresses, and a plurality ofdestination addresses in a storage system, for network traffic;

determining that a hash algorithm to be used across the plurality ofsource addresses for load-balancing the network traffic to the pluralityof destination addresses more closely meets one or more load-balancingcriteria than at least one other hash algorithm of a plurality of hashalgorithms; and

distributing the network traffic from the plurality of source addresses,to the plurality of destination addresses in the storage system, withload-balancing according to the determined hash algorithm.

2. The method of claim 1, wherein:

each of the plurality of source addresses is an Internet protocol (IP)address, a media access control (MAC) address, or a port address;

each of the plurality of destination addresses in the storage system isan Internet protocol (IP) address, a media access control (MAC) address,or a port address; and

the one or more load-balancing criteria comprises a value for evennessof load-balancing.

3. The method of claim 1, wherein:

the hash algorithm is further to be used across portions of networkpackets of the network traffic; and

the determining regarding the hash algorithm comprises determining thatthe hash algorithm to be used across the portions of the network packetsof the network traffic, including the plurality of source addresses,more closely meets the one or more load-balancing criteria than the atleast one other hash algorithm of the plurality of hash algorithms.

4. The method of claim 1, further comprising:

arranging the plurality of destination addresses in the storage systemas a link aggregation group (LAG), multi-link aggregation group (MLAG),or a multi-chassis link aggregation group (MCLAG).

5. The method of claim 1, wherein the determining that the hashalgorithm more closely meets the one or more load-balancing criteriathan at least one other hash algorithm of the plurality of hashalgorithms comprises selecting the hash algorithm from among theplurality of hash algorithms through machine learning or artificialintelligence.

6. The method of claim 1, further comprising:

determining that the plurality of source addresses for the plurality ofdestination addresses comprises a new connection, wherein thedetermining regarding the hash algorithm is in response to thedetermining regarding the new connection.

7. The method of claim 1, wherein the determining regarding the hashalgorithm is performed during an administrative window, installing,upgrading, or one or more new connections.

8. The method of claim 1, wherein the one or more load-balancingcriteria comprises a value for rebalancing stored data in the storagesystem.

9. A tangible, non-transitory, computer-readable media havinginstructions thereupon which, when executed by a processor, cause theprocessor to perform a method comprising:

determining a plurality of source addresses, and a plurality ofdestination addresses in a storage system, for network traffic;

determining, from a plurality of hash algorithms, that a hash algorithmto be used across the plurality of source addresses for load-balancingthe network traffic to the plurality of destination addresses moreclosely meets a value for data storage balancing or network trafficload-balancing than at least one other hash algorithm of the pluralityof hash algorithms; and

distributing the network traffic from the plurality of source addresses,to the plurality of destination addresses in the storage system, withload-balancing according to the determined hash algorithm.

10. The computer-readable media of claim 9, wherein the method furthercomprises:

arranging the plurality of destination addresses in the storage systemas a link aggregation group (LAG), multi-link aggregation group (MLAG),or a multi-chassis link aggregation group (MCLAG).

11. The computer-readable media of claim 9, wherein the determining thatthe hash algorithm more closely meets the value for data storagebalancing or network traffic load-balancing than at least one other hashalgorithm of the plurality of hash algorithms comprises selecting thehash algorithm from among the plurality of hash algorithms throughmachine learning or artificial intelligence.

12. The computer-readable media of claim 9, wherein the method furthercomprises:

determining that the plurality of source addresses for the plurality ofdestination addresses comprises a new connection, wherein thedetermining regarding the hash algorithm is in response to thedetermining regarding the new connection.

13. The computer-readable media of claim 9, wherein the determiningregarding the hash algorithm is to be performed during or responsive toan administrative window, installing, upgrading, or one or more newconnections.

14. A storage system, comprising:

a plurality of blades or nodes having access to storage memory;

a processing device, to:

determining a plurality of source addresses, and a plurality ofdestination addresses in the plurality of blades or nodes, for networktraffic;

determining that a hash algorithm to be used across the plurality ofsource addresses for load-balancing the network traffic to the pluralityof destination addresses more closely meets one or more load-balancingcriteria than at least one other hash algorithm of a plurality of hashalgorithms; and

distributing the network traffic from the plurality of source addresses,to the plurality of destination addresses in the storage system, withload-balancing according to the determined hash algorithm.

15. The storage system of claim 14, wherein:

each of the plurality of source addresses is an Internet protocol (IP)address, a media access control (MAC) address, or a port address;

each of the plurality of destination addresses in the storage system isan Internet protocol (IP) address, a media access control (MAC) address,or a port address; and

the one or more load-balancing criteria comprises a value for evennessof load-balancing.

16. The storage system of claim 14, wherein:

the hash algorithm is further to be used across portions of networkpackets of the network traffic; and

the determining regarding the hash algorithm comprises determining thatthe hash algorithm to be used across the portions of the network packetsof the network traffic, including the plurality of source addresses,more closely meets the one or more load-balancing criteria than the atleast one other hash algorithm of the plurality of hash algorithms.

17. The storage system of claim 14, further comprising:

arranging the plurality of destination addresses in the storage systemas a link aggregation group (LAG), multi-link aggregation group (MLAG),or a multi-chassis link aggregation group (MCLAG).

18. The storage system of claim 14, wherein the determining that thehash algorithm more closely meets the one or more load-balancingcriteria than at least one other hash algorithm of the plurality of hashalgorithms comprises selecting the hash algorithm from among theplurality of hash algorithms through machine learning or artificialintelligence.

19. The storage system of claim 14, wherein the determining regardingthe hash algorithm is performed responsive to an administrative window,installing, upgrading, or one or more new connections.

20. The storage system of claim 14, wherein the one or moreload-balancing criteria comprises a value for rebalancing stored data inthe storage system.

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method, comprising: identifying a plurality ofsource addresses, and a plurality of destination addresses in a storagesystem, for network traffic; determining that a hash algorithm to beused across the plurality of source addresses for load-balancing thenetwork traffic to the plurality of destination addresses more closelymeets one or more load-balancing criteria than at least one other hashalgorithm of a plurality of hash algorithms; and distributing thenetwork traffic from the plurality of source addresses, to the pluralityof destination addresses in the storage system, with load-balancingaccording to the determined hash algorithm.
 2. The method of claim 1,wherein: each of the plurality of source addresses is one of an Internetprotocol (IP) address, a media access control (MAC) address, or a portaddress and the one or more load-balancing criteria comprises a valuefor evenness of load-balancing.
 3. The method of claim 1, wherein thehash algorithm is further to be used across portions of network packetsof the network traffic and the determining regarding the hash algorithmcomprises determining that the hash algorithm to be used across theportions of the network packets of the network traffic, including theplurality of source addresses, more closely meets the one or moreload-balancing criteria than the at least one other hash algorithm ofthe plurality of hash algorithms.
 4. The method of claim 1, furthercomprising: arranging the plurality of destination addresses in thestorage system as a link aggregation group (LAG), multi-link aggregationgroup (MLAG), or a multi-chassis link aggregation group (MCLAG).
 5. Themethod of claim 1, wherein the determining that the hash algorithm moreclosely meets the one or more load-balancing criteria than at least oneother hash algorithm of the plurality of hash algorithms comprisesselecting the hash algorithm from among the plurality of hash algorithmsthrough machine learning or artificial intelligence.
 6. The method ofclaim 1, further comprising: determining that the plurality of sourceaddresses for the plurality of destination addresses comprises a newconnection, wherein the determining regarding the hash algorithm is inresponse to the determining regarding the new connection.
 7. The methodof claim 1, wherein the determining regarding the hash algorithm isperformed during an administrative window, installing, upgrading, or oneor more new connections.
 8. The method of claim 1, wherein the one ormore load-balancing criteria comprises a value for rebalancing storeddata in the storage system.
 9. A tangible, non-transitory,computer-readable media having instructions thereupon which, whenexecuted by a processor, cause the processor to perform a methodcomprising: identifying a plurality of source addresses, and a pluralityof destination addresses in a storage system, for network traffic;determining, from a plurality of hash algorithms, that a hash algorithmto be used across the plurality of source addresses for load-balancingthe network traffic to the plurality of destination addresses moreclosely meets a value for data storage balancing or network trafficload-balancing than at least one other hash algorithm of the pluralityof hash algorithms; and distributing the network traffic from theplurality of source addresses, to the plurality of destination addressesin the storage system, with load-balancing according to the determinedhash algorithm.
 10. The computer-readable media of claim 9, wherein themethod further comprises: arranging the plurality of destinationaddresses in the storage system as a link aggregation group (LAG),multi-link aggregation group (MLAG), or a multi-chassis link aggregationgroup (MCLAG).
 11. The computer-readable media of claim 9, wherein thedetermining that the hash algorithm more closely meets the value fordata storage balancing or network traffic load-balancing than at leastone other hash algorithm of the plurality of hash algorithms comprisesselecting the hash algorithm from among the plurality of hash algorithmsthrough machine learning or artificial intelligence.
 12. Thecomputer-readable media of claim 9, wherein the method furthercomprises: determining that the plurality of source addresses for theplurality of destination addresses comprises a new connection, whereinthe determining regarding the hash algorithm is in response to thedetermining regarding the new connection.
 13. The computer-readablemedia of claim 9, wherein the determining regarding the hash algorithmis to be performed during or responsive to an administrative window,installing, upgrading, or one or more new connections.
 14. A storagesystem, comprising: a plurality of blades or nodes having access tostorage memory; a processing device, to: identify a plurality of sourceaddresses, and a plurality of destination addresses in the plurality ofblades or nodes, for network traffic; determine that a hash algorithm tobe used across the plurality of source addresses for load-balancing thenetwork traffic to the plurality of destination addresses more closelymeets one or more load-balancing criteria than at least one other hashalgorithm of a plurality of hash algorithms; and distribute the networktraffic from the plurality of source addresses, to the plurality ofdestination addresses in the storage system, with load-balancingaccording to the determined hash algorithm.
 15. The storage system ofclaim 14, wherein: each of the plurality of source addresses is anInternet protocol (IP) address, a media access control (MAC) address, ora port address and the one or more load-balancing criteria comprises avalue for evenness of load-balancing.
 16. The storage system of claim14, wherein the hash algorithm is further to be used across portions ofnetwork packets of the network traffic and the determining regarding thehash algorithm comprises determining that the hash algorithm to be usedacross the portions of the network packets of the network traffic,including the plurality of source addresses, more closely meets the oneor more load-balancing criteria than the at least one other hashalgorithm of the plurality of hash algorithms.
 17. The storage system ofclaim 14, further comprising: arranging the plurality of destinationaddresses in the storage system as a link aggregation group (LAG),multi-link aggregation group (MLAG), or a multi-chassis link aggregationgroup (MCLAG).
 18. The storage system of claim 14, wherein determiningthat the hash algorithm more closely meets the one or moreload-balancing criteria than at least one other hash algorithm of theplurality of hash algorithms comprises selecting the hash algorithm fromamong the plurality of hash algorithms through machine learning orartificial intelligence.
 19. The storage system of claim 14, whereindetermining regarding the hash algorithm is performed responsive toupgrading one or more new connections.
 20. The storage system of claim14, wherein the one or more load-balancing criteria comprises a valuefor rebalancing stored data in the storage system.